However, in the presence of well-behaved noise, as in logistic regression, our bounds are suboptimal and require that the fraction of mislabeled data points decays to zero. By contrast, regularized estimators [1]} are able to achieve faster convergence rates in such settings and do not require that the fraction of mislabeled data is asymptotically vanishing.
